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Hereditas(Beijing) ›› 2020, Vol. 42 ›› Issue (12): 1211-1220.doi: 10.16288/j.yczz.20-310

• Genetics Teaching • Previous Articles     Next Articles

How to teach genetic drift

Chunming Wang, Changjun Lin, Huyuan Feng   

  1. School of Life Sciences, Lanzhou University, Lanzhou 730000, China
  • Received:2020-09-24 Revised:2020-11-29 Online:2020-12-17 Published:2020-12-01
  • Supported by:
    Supported by Research Project of Ideological and Political Work in Schools in Gansu Province No(2020XXSZGZYBKT04)

Abstract:

Genetic drift is one of the four important factors affecting population genetic balance. Because its form of action is not as apparent as mutation, selection, and migration, which are intuitive and easy to understand, there are potential difficulties in understanding and mastering genetic drift. A particularly prominent problem is that the current introduction of genetic drift contents in textbooks is systematically insufficient. They are either even too rough, or completely neglecting the mathematical foundation such as the binomial theorem, resulting in long-term inadequate learning of genetic drift. In this paper, we summarize the five basic attributes of genetic drift, namely inherent, universal, random, non-directional, and regular features. Based on the concept that the genetic basis of genetic drift is the free combination of male and female gametes, we pointed out that the attribute of random sampling error is the inherent essential feature of genetic drift. Then step by step, from an extremely small population consisting of only one individual (N = 1), we deduced that the effect of genetic drift decreased while population size increased. Through introducing the mathematical model of the binomial theorem, the characteristics of the binomial distribution, and the results of computer simulations, the effect of genetic drift is visually and intuitively displayed to help the teaching the concept of genetic drift.

Key words: genetic drift, population, genetic equilibrium, binomial theorem, binomial distribution, sampling error